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Feng, Wentao (2008) INFERENCE, POWER AND SAMPLE SIZE FOR ADAPTIVE TWO-STAGETREATMENT STRATEGIES. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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An adaptive treatment strategy (ATS) is defined as a sequence of treatments and intermediate responses. ATS' arise when chronic diseases such as cancer and depression are treated over time with various treatment alternatives depending on intermediate responses to earlier treatments. For example, in two-stage adaptive treatment strategies, patients receive one of the induction treatments followed by a maintenance therapy given that the patients responded to the induction treatment they received. Clinical trials are often designed to compare adaptive treatment strategies based on appropriate designs such as sequential randomization designs. One of the main objectives of these trials is to compare two or more treatment strategies in terms of largest patient benefit, such as prolonged survival.Statistical inference from such trials needs to account for the sequential randomization structure of the design. Recent literature suggests several methods of estimation. A comparative review of currently available inferential procedures for analyzing data from such trials is presented. A sample size formula is introduced for comparing the survival probabilities under two treatment strategies sharing the same initial treatment. The formula is based on the large sample properties of inverse-probability- weighted estimator. Monte Carlo simulation study shows strong evidence that the proposed sample size formula guarantees desired power, regardless of the true distributions of survival times. To test for a difference in the effects of different induction and maintenance treatment combinations, a supremum weighted log-rank test is proposed. The test is applied to a dataset from a two-stage randomized trial and the results are compared to those obtained using a standard weighted log-rank test. A sample-size formula is derived based on the limiting distribution of the supremum weighted log-rank statistic. Simulation studies show that the proposed test provides sample sizes which are close to those obtained by standard weighted log-rank test under a proportional hazard alternative. However, the proposed test is more powerful than the standard weighted log-rank test under non-proportional hazard alternatives.The public health significance of this work is to provide a practical guidance of sample size determination and a test procedure in clinical trials that adopt two stage randomization designs.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWahed, Abdus Swahed@pitt.eduWAHED
Committee MemberChang, Chung-Chou Hochangj@pitt.eduCHANGJ
Committee MemberRockette, Howard Eherbst@pitt.eduHERBST
Committee MemberJeong, Jong-Hyeonjeong@nsabp.pitt.eduJJEONG
Date: 24 June 2008
Date Type: Completion
Defense Date: 3 April 2008
Approval Date: 24 June 2008
Submission Date: 10 April 2008
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Adaptive treatment strategy; Brownian motion; Counting process; potential outcomes; proportional hazard assumption; inverse-probability weighting; Supremum log-rank statistics
Other ID:, etd-04102008-155704
Date Deposited: 10 Nov 2011 19:35
Last Modified: 15 Nov 2016 13:39


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